Converting documents for LLM processing — A modern approach

Processing documents for LLM training or AI pipelines often means dealing with thousands of files in various formats.

After encountering this challenge repeatedly in my work, I developed Monkt - a tool that helps transform documents and URLs into structured formats like JSON or Markdown.

The common challenges

• Maintaining format consistency across different document types
• Preserving structural elements (headers, tables, relationships)
• Scaling the conversion process efficiently

Best practices for document processing

• Preserve semantic structure: Maintain document hierarchy, relationships between headers, sections, and lists.
• Handle mixed content: Process both text and non-text elements consistently, including images and tables.
• Implement quality validation: Use automated checks and schemas to catch structural errors.
• Design for scale: Utilize batch operations, parallel processing, and caching mechanisms.

A modern approach

Rather than combining multiple Python libraries (pdf2text, docx, BeautifulSoup, markitdown), modern document processing should focus on:

• Automated format handling
• Consistent structure preservation
• Flexible output formats (Markdown/JSON)
• Efficient caching for improved performance

The quality of your document conversion directly impacts both model training efficiency and inference accuracy.